Error Prediction Based on Correlation Using Event Records

ABSTRACT

A method includes receiving an error message including first information associated with a first reported error and a time at which the first reported error was detected. First stored event records associated with second reported errors are identified. The first stored event records include second information describing previously reported errors that occurred within a predetermined time prior to the time at which the first reported error was detected. The method determines, based on the first information and the second information, whether a correlation exists among one or more of the previously reported errors and the first reported error. In response to determining that the correlation exists, generating an error correlation report predicting occurrence of a third error.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 120 as a continuation-in-part of U.S. Utility applicationSer. No. 17/236,144, entitled “IDENTIFYING A PARENT EVENT ASSOCIATEDWITH CHILD ERROR STATES,” filed Apr. 21, 2021, which is a continuationof U.S. Utility application Ser. No. 16/148,012, entitled “HIERARCHICALEVENT TREE,” filed Oct. 1, 2018, now U.S. Pat. No. 11,016,702 on05/25/2021, which is a continuation-in-part of U.S. Utility applicationSer. No. 15/716,169, entitled “UNIFIED LOGS AND DEVICE STATISTICS” filedSep. 26, 2017, now U.S. Pat. No. 10,678,619 on 06/09/2020, which claimspriority pursuant to 35 U.S.C. § 120 as a continuation-in-part of U.S.Utility patent application Ser. No. 13/547,769, entitled “GENERATINGDISPERSED STORAGE NETWORK EVENT RECORDS, filed Jul. 12, 2012, now U.S.Pat. No. 9,852,017 on Dec. 26, 2017, which claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 61/512,122,entitled “PROCESSING EVENT INFORMATION IN A DISPERSED STORAGE NETWORK,”filed Jul. 27, 2011, all of which are hereby incorporated herein byreference in their entirety and made part of the present U.S. Utilitypatent application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computing systems and moreparticularly to data storage solutions within such computing systems.

Description of Related Art

Computers are known to communicate, process, and store data. Suchcomputers range from wireless smart phones to data centers that supportmillions of web searches, stock trades, or on-line purchases every day.In general, a computing system generates data and/or manipulates datafrom one form into another. For instance, an image sensor of thecomputing system generates raw picture data and, using an imagecompression program (e.g., JPEG, MPEG, etc.), the computing systemmanipulates the raw picture data into a standardized compressed image.

With continued advances in processing speed and communication speed,computers are capable of processing real time multimedia data forapplications ranging from simple voice communications to streaming highdefinition video. As such, general-purpose information appliances arereplacing purpose-built communications devices (e.g., a telephone). Forexample, smart phones can support telephony communications but they arealso capable of text messaging and accessing the internet to performfunctions including email, web browsing, remote applications access, andmedia communications (e.g., telephony voice, image transfer, musicfiles, video files, real time video streaming. etc.).

Each type of computer is constructed and operates in accordance with oneor more communication, processing, and storage standards. As a result ofstandardization and with advances in technology, more and moreinformation content is being converted into digital formats. Forexample, more digital cameras are now being sold than film cameras, thusproducing more digital pictures. As another example, web-basedprogramming is becoming an alternative to over the air televisionbroadcasts and/or cable broadcasts. As further examples, papers, books,video entertainment, home video, etc. are now being stored digitally,which increases the demand on the storage function of computers.

A typical computer storage system includes one or more memory devicesaligned with the needs of the various operational aspects of thecomputer's processing and communication functions. Generally, theimmediacy of access dictates what type of memory device is used. Forexample, random access memory (RAM) memory can be accessed in any randomorder with a constant response time, thus it is typically used for cachememory and main memory. By contrast, memory device technologies thatrequire physical movement such as magnetic disks, tapes, and opticaldiscs, have a variable response time as the physical movement can takelonger than the data transfer, thus they are typically used forsecondary memory (e.g., hard drive, backup memory, etc.).

A computer's storage system will be compliant with one or more computerstorage standards that include, but are not limited to, network filesystem (NFS), flash file system (FFS), disk file system (DFS), smallcomputer system interface (SCSI), internet small computer systeminterface (iSCSI), file transfer protocol (FTP), and web-baseddistributed authoring and versioning (WebDAV). These standards specifythe data storage format (e.g., files, data objects, data blocks,directories, etc.) and interfacing between the computer's processingfunction and its storage system, which is a primary function of thecomputer's memory controller.

Despite the standardization of the computer and its storage system,memory devices fail; especially commercial grade memory devices thatutilize technologies incorporating physical movement (e.g., a discdrive). For example, it is fairly common for a disc drive to routinelysuffer from bit level corruption and to completely fail after threeyears of use. One solution is to a higher-grade disc drive, which addssignificant cost to a computer.

Another solution is to utilize multiple levels of redundant disc drivesto replicate the data into two or more copies. One such redundant driveapproach is called redundant array of independent discs (RAID). In aRAID device, a RAID controller adds parity data to the original databefore storing it across the array. The parity data is calculated fromthe original data such that the failure of a disc will not result in theloss of the original data. For example, RAID 5 uses three discs toprotect data from the failure of a single disc. The parity data, andassociated redundancy overhead data, reduces the storage capacity ofthree independent discs by one third (e.g., n−1=capacity). RAID 6 canrecover from a loss of two discs and requires a minimum of four discswith a storage capacity of n−2.

While RAID addresses the memory device failure issue, it is not withoutits own failures issues that affect its effectiveness, efficiency andsecurity. For instance, as more discs are added to the array, theprobability of a disc failure increases, which increases the demand formaintenance. For example, when a disc fails, it needs to be manuallyreplaced before another disc fails and the data stored in the RAIDdevice is lost. To reduce the risk of data loss, data on a RAID deviceis typically copied on to one or more other RAID devices. While thisaddresses the loss of data issue, it raises a security issue sincemultiple copies of data are available, which increases the chances ofunauthorized access. Further, as the amount of data being stored grows,the overhead of RAID devices becomes a non-trivial efficiency issue.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an embodiment of a distributedstorage processing unit in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of a grid module inaccordance with the present invention;

FIG. 5 is a diagram of an example embodiment of error coded data slicecreation in accordance with the present invention;

FIG. 6 is a schematic block diagram of another embodiment of a computingsystem in accordance with the present invention;

FIG. 7A is a diagram illustrating an example of an event record inaccordance with the present invention;

FIG. 7B is a diagram illustrating an example of a log record inaccordance with the present invention;

FIG. 7C is a diagram illustrating an example of a statistics record inaccordance with the present invention;

FIG. 8 is a diagram illustrating an example of a hierarchal event recordrepresentation in accordance with the present invention;

FIG. 9A is a flowchart illustrating an example of generating eventrecords in accordance with the present invention;

FIG. 9B is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 9C is a flowchart illustrating an example of collecting eventrecords in accordance with the present invention;

FIG. 10A is a flowchart illustrating an example of generating a logrecord in accordance with the present invention;

FIG. 10B is a flowchart illustrating an example of generating astatistics record in accordance with the present invention;

FIG. 10C is a flowchart illustrating an example of correlating astatistics record and a log record in accordance with the presentinvention;

FIG. 11 is a flowchart illustrating an example of generating arepresentation of event records in accordance with the presentinvention;

FIG. 12 is a flowchart illustrating an example of analyzing eventrecords in accordance with the present invention;

FIG. 13 is a flowchart illustrating an example of combining adjunctinformation with event records in accordance with the present invention;

FIG. 14 is a flowchart illustrating another example of analyzing eventrecords in accordance with the present invention;

FIG. 15A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 15B is a flowchart illustrating an example of determining a causeof an error in accordance with the present invention;

FIG. 16A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention;

FIG. 16B is a flowchart illustrating an example of identifying a sliceto rebuild in accordance with the present invention;

FIG. 17 is a flowchart illustrating an example of correlating errors inaccordance with the present invention;

FIG. 18A is a schematic block diagram of another embodiment of acomputing system in accordance with the present invention; and

FIG. 18B is a flowchart illustrating an example of modifying eventrecords in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of a computing system 10 thatincludes one or more of a first type of user devices 12, one or more ofa second type of user devices 14, at least one distributed storage (DS)processing unit 16, at least one DS managing unit 18, at least onestorage integrity processing unit 20, and a distributed storage network(DSN) memory 22 coupled via a network 24. The network 24 may include oneor more wireless and/or wire lined communication systems; one or moreprivate intranet systems and/or public internet systems; and/or one ormore local area networks (LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of distributed storage (DS) units36 for storing data of the system. Each of the DS units 36 includes aprocessing module and memory and may be located at a geographicallydifferent site than the other DS units (e.g., one in Chicago, one inMilwaukee, etc.).

Each of the user devices 12-14, the DS processing unit 16, the DSmanaging unit 18, and the storage integrity processing unit 20 may be aportable computing device (e.g., a social networking device, a gamingdevice, a cell phone, a smart phone, a personal digital assistant, adigital music player, a digital video player, a laptop computer, ahandheld computer, a video game controller, and/or any other portabledevice that includes a computing core) and/or a fixed computing device(e.g., a personal computer, a computer server, a cable set-top box, asatellite receiver, a television set, a printer, a fax machine, homeentertainment equipment, a video game console, and/or any type of homeor office computing equipment). Such a portable or fixed computingdevice includes a computing core 26 and one or more interfaces 30, 32,and/or 33. An embodiment of the computing core 26 will be described withreference to FIG. 2 .

With respect to the interfaces, each of the interfaces 30, 32, and 33includes software and/or hardware to support one or more communicationlinks via the network 24 indirectly and/or directly. For example,interfaces 30 support a communication link (wired, wireless, direct, viaa LAN, via the network 24, etc.) between the first type of user device14 and the DS processing unit 16. As another example, DSN interface 32supports a plurality of communication links via the network 24 betweenthe DSN memory 22 and the DS processing unit 16, the first type of userdevice 12, and/or the storage integrity processing unit 20. As yetanother example, interface 33 supports a communication link between theDS managing unit 18 and any one of the other devices and/or units 12,14, 16, 20, and/or 22 via the network 24.

In general and with respect to data storage, the system 10 supportsthree primary functions: distributed network data storage management,distributed data storage and retrieval, and data storage integrityverification. In accordance with these three primary functions, data canbe distributedly stored in a plurality of physically different locationsand subsequently retrieved in a reliable and secure manner regardless offailures of individual storage devices, failures of network equipment,the duration of storage, the amount of data being stored, attempts athacking the data, etc.

The DS managing unit 18 performs distributed network data storagemanagement functions, which include establishing distributed datastorage parameters, performing network operations, performing networkadministration, and/or performing network maintenance. The DS managingunit 18 establishes the distributed data storage parameters (e.g.,allocation of virtual DSN memory space, distributed storage parameters,security parameters, billing information, user profile information,etc.) for one or more of the user devices 12-14 (e.g., established forindividual devices, established for a user group of devices, establishedfor public access by the user devices, etc.). For example, the DSmanaging unit 18 coordinates the creation of a vault (e.g., a virtualmemory block) within the DSN memory 22 for a user device (for a group ofdevices, or for public access). The DS managing unit 18 also determinesthe distributed data storage parameters for the vault. In particular,the DS managing unit 18 determines a number of slices (e.g., the numberthat a data segment of a data file and/or data block is partitioned intofor distributed storage) and a read threshold value (e.g., the minimumnumber of slices required to reconstruct the data segment).

As another example, the DS managing module 18 creates and stores,locally or within the DSN memory 22, user profile information. The userprofile information includes one or more of authentication information,permissions, and/or the security parameters. The security parameters mayinclude one or more of encryption/decryption scheme, one or moreencryption keys, key generation scheme, and data encoding/decodingscheme.

As yet another example, the DS managing unit 18 creates billinginformation for a particular user, user group, vault access, publicvault access, etc. For instance, the DS managing unit 18 tracks thenumber of times user accesses a private vault and/or public vaults,which can be used to generate a per-access bill. In another instance,the DS managing unit 18 tracks the amount of data stored and/orretrieved by a user device and/or a user group, which can be used togenerate a per-data-amount bill.

The DS managing unit 18 also performs network operations, networkadministration, and/or network maintenance. As at least part ofperforming the network operations and/or administration, the DS managingunit 18 monitors performance of the devices and/or units of the system10 for potential failures, determines the devices and/or unit'sactivation status, determines the devices' and/or units' loading, andany other system level operation that affects the performance level ofthe system 10. For example, the DS managing unit 18 receives andaggregates network management alarms, alerts, errors, statusinformation, performance information, and messages from the devices12-14 and/or the units 16, 20, 22. For example, the DS managing unit 18receives a simple network management protocol (SNMP) message regardingthe status of the DS processing unit 16.

The DS managing unit 18 performs the network maintenance by identifyingequipment within the system 10 that needs replacing, upgrading,repairing, and/or expanding. For example, the DS managing unit 18determines that the DSN memory 22 needs more DS units 36 or that one ormore of the DS units 36 needs updating.

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has a data file 38 and/or data block 40 tostore in the DSN memory 22, it send the data file 38 and/or data block40 to the DS processing unit 16 via its interface 30. As will bedescribed in greater detail with reference to FIG. 2 , the interface 30functions to mimic a conventional operating system (OS) file systeminterface (e.g., network file system (NFS), flash file system (FFS),disk file system (DFS), file transfer protocol (FTP), web-baseddistributed authoring and versioning (WebDAV), etc.) and/or a blockmemory interface (e.g., small computer system interface (SCSI), internetsmall computer system interface (iSCSI), etc.). In addition, theinterface 30 may attach a user identification code (ID) to the data file38 and/or data block 40.

The DS processing unit 16 receives the data file 38 and/or data block 40via its interface 30 and performs a distributed storage (DS) process 34thereon (e.g., an error coding dispersal storage function). The DSprocessing 34 begins by partitioning the data file 38 and/or data block40 into one or more data segments, which is represented as Y datasegments. For example, the DS processing 34 may partition the data file38 and/or data block 40 into a fixed byte size segment (e.g., 2¹ to2^(n) bytes, where n=>2) or a variable byte size (e.g., change byte sizefrom segment to segment, or from groups of segments to groups ofsegments, etc.).

For each of the Y data segments, the DS processing 34 error encodes(e.g., forward error correction (FEC), information dispersal algorithm,or error correction coding) and slices (or slices then error encodes)the data segment into a plurality of error coded (EC) data slices 42-48,which is represented as X slices per data segment. The number of slices(X) per segment, which corresponds to a number of pillars n, is set inaccordance with the distributed data storage parameters and the errorcoding scheme. For example, if a Reed-Solomon (or other FEC scheme) isused in an n/k system, then a data segment is divided into n slices,where k number of slices is needed to reconstruct the original data(i.e., k is the threshold). As a few specific examples, the n/k factormay be 5/3; 6/4; 8/6; 8/5; 16/10.

For each slice 42-48, the DS processing unit 16 creates a unique slicename and appends it to the corresponding slice 42-48. The slice nameincludes universal DSN memory addressing routing information (e.g.,virtual memory addresses in the DSN memory 22) and user-specificinformation (e.g., user ID, file name, data block identifier, etc.).

The DS processing unit 16 transmits the plurality of EC slices 42-48 toa plurality of DS units 36 of the DSN memory 22 via the DSN interface 32and the network 24. The DSN interface 32 formats each of the slices fortransmission via the network 24. For example, the DSN interface 32 mayutilize an internet protocol (e.g., TCP/IP, etc.) to packetize theslices 42-48 for transmission via the network 24.

The number of DS units 36 receiving the slices 42-48 is dependent on thedistributed data storage parameters established by the DS managing unit18. For example, the DS managing unit 18 may indicate that each slice isto be stored in a different DS unit 36. As another example, the DSmanaging unit 18 may indicate that like slice numbers of different datasegments are to be stored in the same DS unit 36. For example, the firstslice of each of the data segments is to be stored in a first DS unit36, the second slice of each of the data segments is to be stored in asecond DS unit 36, etc. In this manner, the data is encoded anddistributedly stored at physically diverse locations to improved datastorage integrity and security.

Each DS unit 36 that receives a slice 42-48 for storage translates thevirtual DSN memory address of the slice into a local physical addressfor storage. Accordingly, each DS unit 36 maintains a virtual tophysical memory mapping to assist in the storage and retrieval of data.

The first type of user device 12 performs a similar function to storedata in the DSN memory 22 with the exception that it includes the DSprocessing. As such, the device 12 encodes and slices the data fileand/or data block it has to store. The device then transmits the slices11 to the DSN memory via its DSN interface 32 and the network 24.

For a second type of user device 14 to retrieve a data file or datablock from memory, it issues a read command via its interface 30 to theDS processing unit 16. The DS processing unit 16 performs the DSprocessing 34 to identify the DS units 36 storing the slices of the datafile and/or data block based on the read command. The DS processing unit16 may also communicate with the DS managing unit 18 to verify that theuser device 14 is authorized to access the requested data.

Assuming that the user device is authorized to access the requesteddata, the DS processing unit 16 issues slice read commands to at least athreshold number of the DS units 36 storing. The requested data (e.g.,to at least 10 DS units for a 16/10 error coding scheme). Each of the DSunits 36 receiving the slice read command, verifies the command,accesses its virtual to physical memory mapping, retrieves the requestedslice, or slices, and transmits it to the DS processing unit 16.

Once the DS processing unit 16 has received a read threshold number ofslices for a data segment, it performs an error decoding function andde-slicing to reconstruct the data segment. When Y number of datasegments has been reconstructed, the DS processing unit 16 provides thedata file 38 and/or data block 40 to the user device 14. Note that thefirst type of user device 12 performs a similar process to retrieve adata file and/or data block.

The storage integrity processing unit 20 performs the third primaryfunction of data storage integrity verification. In general, the storageintegrity processing unit 20 periodically retrieves slices 45, and/orslice names, of a data file or data block of a user device to verifythat one or more slices have not been corrupted or lost (e.g., the DSunit failed). The retrieval process mimics the read process previouslydescribed.

If the storage integrity processing unit 20 determines that one or moreslices is corrupted or lost, it rebuilds the corrupted or lost slice(s)in accordance with the error coding scheme. The storage integrityprocessing unit 20 stores the rebuild slice, or slices, in theappropriate DS unit(s) 36 in a manner that mimics the write processpreviously described.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,at least one IO device interface module 62, a read only memory (ROM)basic input output system (BIOS) 64, and one or more memory interfacemodules. The memory interface module(s) includes one or more of auniversal serial bus (USB) interface module 66, a host bus adapter (HBA)interface module 68, a network interface module 70, a flash interfacemodule 72, a hard drive interface module 74, and a DSN interface module76. Note the DSN interface module 76 and/or the network interface module70 may function as the interface 30 of the user device 14 of FIG. 1 .Further note that the 10 device interface module 62 and/or the memoryinterface modules may be collectively or individually referred to as 10ports.

FIG. 3 is a schematic block diagram of an embodiment of a dispersedstorage (DS) processing module 34 of user device 12 and/or of the DSprocessing unit 16. The DS processing module 34 includes a gatewaymodule 78, an access module 80, a grid module 82, and a storage module84. The DS processing module 34 may also include an interface 30 and theDSnet interface 32 or the interfaces 68 and/or 70 may be part of user 12or of the DS processing unit 14. The DS processing module 34 may furtherinclude a bypass/feedback path between the storage module 84 to thegateway module 78. Note that the modules 78-84 of the DS processingmodule 34 may be in a single unit or distributed across multiple units.

In an example of storing data, the gateway module 78 receives anincoming data object that includes a user ID field 86, an object namefield 88, and the data field 40 and may also receive correspondinginformation that includes a process identifier (e.g., an internalprocess/application ID), metadata, a file system directory, a blocknumber, a transaction message, a user device identity (ID), a dataobject identifier, a source name, and/or user information. The gatewaymodule 78 authenticates the user associated with the data object byverifying the user ID 86 with the managing unit 18 and/or anotherauthenticating unit.

When the user is authenticated, the gateway module 78 obtains userinformation from the management unit 18, the user device, and/or theother authenticating unit. The user information includes a vaultidentifier, operational parameters, and user attributes (e.g., userdata, billing information, etc.). A vault identifier identifies a vault,which is a virtual memory space that maps to a set of DS storage units36. For example, vault 1 (i.e., user 1's DSN memory space) includeseight DS storage units (X=8 wide) and vault 2 (i.e., user 2's DSN memoryspace) includes sixteen DS storage units (X=16 wide). The operationalparameters may include an error coding algorithm, the width n (number ofpillars X or slices per segment for this vault), a read threshold T, awrite threshold, an encryption algorithm, a slicing parameter, acompression algorithm, an integrity check method, caching settings,parallelism settings, and/or other parameters that may be used to accessthe DSN memory layer.

The gateway module 78 uses the user information to assign a source name35 to the data. For instance, the gateway module 60 determines thesource name 35 of the data object 40 based on the vault identifier andthe data object. For example, the source name may contain a fileidentifier (ID), a vault generation number, a reserved field, and avault identifier (ID). As another example, the gateway module 78 maygenerate the file ID based on a hash function of the data object 40.Note that the gateway module 78 may also perform message conversion,protocol conversion, electrical conversion, optical conversion, accesscontrol, user identification, user information retrieval, trafficmonitoring, statistics generation, configuration, management, and/orsource name determination.

The access module 80 receives the data object 40 and creates a series ofdata segments 1 through Y 90-92 in accordance with a data storageprotocol (e.g., file storage system, a block storage system, and/or anaggregated block storage system). The number of segments Y may be chosenor randomly assigned based on a selected segment size and the size ofthe data object. For example, if the number of segments is chosen to bea fixed number, then the size of the segments varies as a function ofthe size of the data object. For instance, if the data object is animage file of 4,194,304 eight bit bytes (e.g., 33,554,432 bits) and thenumber of segments Y=131,072, then each segment is 256 bits or 32 bytes.As another example, if segment sized is fixed, then the number ofsegments Y varies based on the size of data object. For instance, if thedata object is an image file of 4,194,304 bytes and the fixed size ofeach segment is 4,096 bytes, the then number of segments Y=1,024. Notethat each segment is associated with the same source name.

The grid module 82 receives the data segments and may manipulate (e.g.,compression, encryption, cyclic redundancy check (CRC), etc.) each ofthe data segments before performing an error coding function of theerror coding dispersal storage function to produce a pre-manipulateddata segment. After manipulating a data segment, if applicable, the gridmodule 82 error encodes (e.g., Reed-Solomon, Convolution encoding,Trellis encoding, etc.) the data segment or manipulated data segmentinto X error coded data slices 42-44.

The value X, or the number of pillars (e.g., X=16), is chosen as aparameter of the error coding dispersal storage function. Otherparameters of the error coding dispersal function include a readthreshold T, a write threshold W, etc. The read threshold (e.g., T=10,when X=16) corresponds to the minimum number of error-free error codeddata slices required to reconstruct the data segment. In other words,the DS processing module 34 can compensate for X-T (e.g., 16−10=6)missing error coded data slices per data segment. The write threshold Wcorresponds to a minimum number of DS storage units that acknowledgeproper storage of their respective data slices before the DS processingmodule indicates proper storage of the encoded data segment. Note thatthe write threshold is greater than or equal to the read threshold for agiven number of pillars (X).

For each data slice of a data segment, the grid module 82 generates aunique slice name 37 and attaches it thereto. The slice name 37 includesa universal routing information field and a vault specific field and maybe 48 bytes (e.g., 24 bytes for each of the universal routinginformation field and the vault specific field). As illustrated, theuniversal routing information field includes a slice index, a vault ID,a vault generation, and a reserved field. The slice index is based onthe pillar number and the vault ID and, as such, is unique for eachpillar (e.g., slices of the same pillar for the same vault for anysegment will share the same slice index). The vault specific fieldincludes a data name, which includes a file ID and a segment number(e.g., a sequential numbering of data segments 1-Y of a simple dataobject or a data block number).

Prior to outputting the error coded data slices of a data segment, thegrid module may perform post-slice manipulation on the slices. Ifenabled, the manipulation includes slice level compression, encryption,CRC, addressing, tagging, and/or other manipulation to improve theeffectiveness of the computing system.

When the error coded data slices of a data segment are ready to beoutputted, the grid module 82 determines which of the DS storage units36 will store the EC data slices based on a dispersed storage memorymapping associated with the user's vault and/or DS storage unitattributes. The DS storage unit attributes may include availability,self-selection, performance history, link speed, link latency,ownership, available DSN memory, domain, cost, a prioritization scheme,a centralized selection message from another source, a lookup table,data ownership, and/or any other factor to optimize the operation of thecomputing system. Note that the number of DS storage units 36 is equalto or greater than the number of pillars (e.g., X) so that no more thanone error coded data slice of the same data segment is stored on thesame DS storage unit 36. Further note that EC data slices of the samepillar number but of different segments (e.g., EC data slice 1 of datasegment 1 and EC data slice 1 of data segment 2) may be stored on thesame or different DS storage units 36.

The storage module 84 performs an integrity check on the outboundencoded data slices and, when successful, identifies a plurality of DSstorage units based on information provided by the grid module 82. Thestorage module 84 then outputs the encoded data slices 1 through X ofeach segment 1 through Y to the DS storage units 36. Each of the DSstorage units 36 stores its EC data slice(s) and maintains a localvirtual DSN address to physical location table to convert the virtualDSN address of the EC data slice(s) into physical storage addresses.

In an example of a read operation, the user device 12 and/or 14 sends aread request to the DS processing unit 14, which authenticates therequest. When the request is authentic, the DS processing unit 14 sendsa read message to each of the DS storage units 36 storing slices of thedata object being read. The slices are received via the DSnet interface32 and processed by the storage module 84, which performs a parity checkand provides the slices to the grid module 82 when the parity check wassuccessful. The grid module 82 decodes the slices in accordance with theerror coding dispersal storage function to reconstruct the data segment.The access module 80 reconstructs the data object from the data segmentsand the gateway module 78 formats the data object for transmission tothe user device.

FIG. 4 is a schematic block diagram of an embodiment of a grid module 82that includes a control unit 73, a pre-slice manipulator 75, an encoder77, a slicer 79, a post-slice manipulator 81, a pre-slice de-manipulator83, a decoder 85, a de-slicer 87, and/or a post-slice de-manipulator 89.Note that the control unit 73 may be partially or completely external tothe grid module 82. For example, the control unit 73 may be part of thecomputing core at a remote location, part of a user device, part of theDS managing unit 18, or distributed amongst one or more DS storageunits.

In an example of write operation, the pre-slice manipulator 75 receivesa data segment 90-92 and a write instruction from an authorized userdevice. The pre-slice manipulator 75 determines if pre-manipulation ofthe data segment 90-92 is required and, if so, what type. The pre-slicemanipulator 75 may make the determination independently or based oninstructions from the control unit 73, where the determination is basedon a computing system-wide predetermination, a table lookup, vaultparameters associated with the user identification, the type of data,security requirements, available DSN memory, performance requirements,and/or other metadata.

Once a positive determination is made, the pre-slice manipulator 75manipulates the data segment 90-92 in accordance with the type ofmanipulation. For example, the type of manipulation may be compression(e.g., Lempel-Ziv-Welch, Huffman, Golomb, fractal, wavelet, etc.),signatures (e.g., Digital Signature Algorithm (DSA), Elliptic Curve DSA,Secure Hash Algorithm, etc.), watermarking, tagging, encryption (e.g.,Data Encryption Standard, Advanced Encryption Standard, etc.), addingmetadata (e.g., time/date stamping, user information, file type, etc.),cyclic redundancy check (e.g., CRC32), and/or other data manipulationsto produce the pre-manipulated data segment.

The encoder 77 encodes the pre-manipulated data segment 92 using aforward error correction (FEC) encoder (and/or other type of erasurecoding and/or error coding) to produce an encoded data segment 94. Theencoder 77 determines which forward error correction algorithm to usebased on a predetermination associated with the user's vault, a timebased algorithm, user direction, DS managing unit direction, controlunit direction, as a function of the data type, as a function of thedata segment 92 metadata, and/or any other factor to determine algorithmtype. The forward error correction algorithm may be Golay,Multidimensional parity, Reed-Solomon, Hamming, Bose Ray ChauduriHocquenghem (BCH), Cauchy-Reed-Solomon, or any other FEC encoder. Notethat the encoder 77 may use a different encoding algorithm for each datasegment 92, the same encoding algorithm for the data segments 92 of adata object, or a combination thereof.

The encoded data segment 94 is of greater size than the data segment 92by the overhead rate of the encoding algorithm by a factor of X/T, whereX is the width or number of slices, and T is the read threshold. In thisregard, the corresponding decoding process can accommodate at most X-Tmissing EC data slices and still recreate the data segment 92. Forexample, if X=16 and T=10, then the data segment 92 will be recoverableas long as 10 or more EC data slices per segment are not corrupted.

The slicer 79 transforms the encoded data segment 94 into EC data slicesin accordance with the slicing parameter from the vault for this userand/or data segment 92. For example, if the slicing parameter is X=16,then the slicer 79 slices each encoded data segment 94 into 16 encodedslices.

The post-slice manipulator 81 performs, if enabled, post-manipulation onthe encoded slices to produce the EC data slices. If enabled, thepost-slice manipulator 81 determines the type of post-manipulation,which may be based on a computing system-wide predetermination,parameters in the vault for this user, a table lookup, the useridentification, the type of data, security requirements, available DSNmemory, performance requirements, control unit directed, and/or othermetadata. Note that the type of post-slice manipulation may includeslice level compression, signatures, encryption, CRC, addressing,watermarking, tagging, adding metadata, and/or other manipulation toimprove the effectiveness of the computing system.

In an example of a read operation, the post-slice de-manipulator 89receives at least a read threshold number of EC data slices and performsthe inverse function of the post-slice manipulator 81 to produce aplurality of encoded slices. The de-slicer 87 de-slices the encodedslices to produce an encoded data segment 94. The decoder 85 performsthe inverse function of the encoder 77 to recapture the data segment90-92. The pre-slice de-manipulator 83 performs the inverse function ofthe pre-slice manipulator 75 to recapture the data segment 90-92.

FIG. 5 is a diagram of an example of slicing an encoded data segment 94by the slicer 79. In this example, the encoded data segment 94 includesthirty-two bits, but may include more or less bits. The slicer 79disperses the bits of the encoded data segment 94 across the EC dataslices in a pattern as shown. As such, each EC data slice does notinclude consecutive bits of the data segment 94 reducing the impact ofconsecutive bit failures on data recovery. For example, if EC data slice2 (which includes bits 1, 5, 9, 13, 17, 25, and 29) is unavailable(e.g., lost, inaccessible, or corrupted), the data segment can bereconstructed from the other EC data slices (e.g., 1, 3 and 4 for a readthreshold of 3 and a width of 4).

FIG. 6 is a schematic block diagram of another embodiment of a computingsystem that includes a user device 12, a dispersed storage (DS)processing unit 16, a DS managing unit 18, and a plurality of DS units36 of a dispersed storage network (DSN). Each of the user device 12, theDS processing unit 16, the DS managing unit 18, and the plurality of DSunits 36 may include an event memory 102. The event memory 102 may beimplemented as a single memory device, a plurality of memory devices,and/or embedded circuitry of a processing module. The memory device maybe a read-only memory, random access memory, volatile memory,non-volatile memory, static memory, dynamic memory, flash memory, cachememory, magnetic disk memory, optical disk memory, and/or any devicethat stores digital information. The event memory 102 stores eventinformation 108 including one or more of event records, log records, andstatistics records.

Each of the user device 12, the DS processing unit 16, the DS managingunit 18, and the plurality of DS units 36 store and/or retrieve eventinformation 108 from any one or more of the event memories 102 of thesystem. The event information 108 may be subsequently utilized todocument and/or analyze operation and/or performance of the computingsystem. Any element of the system may receive event information 108 fromany other element of the system, aggregate the received eventinformation 108 to produce aggregated event information, analyze theaggregated event information, and produce an analysis with regards toperformance of the DSN. For example, the user device 12, the DSprocessing unit 16, and the plurality of DS units 36 processtransactions of the computing system, generate event information 108,store the event information 108 in an associated event memory 102, andsend the event information 108 to the DS managing unit 18 foraggregation and analysis. The event records, log records, and statisticsrecords are discussed in greater detail with reference to FIGS. 7A-7C.

In an example of operation, user device 12 sends a retrieval request 104to the DS processing unit 16, generates retrieval request eventinformation 108, and stores the retrieval request event information 108in the event memory 102 of the user device 12. The DS processing unit 16receives the retrieval request 104, generates received retrieval requestevent information 108, and stores the received retrieval request eventinformation 108 in the event memory 102 of the DS processing unit 16.The DS processing unit 16 processes the retrieval request 104 togenerate a plurality of read requests 110, generates read requestprocessing event information 108 (e.g., for each request), and storesthe read request processing event information 108 in the event memory102 of the DS processing unit 16. The DS processing unit 16 sends theplurality of read requests 110 to the plurality of DS units 36,generates read request sending event information 108 (e.g., for eachrequest), and stores the read request sending event information 108 inthe event memory 102 of the DS processing unit 16. Each DS unit 36 ofthe plurality of DS units 36 receives a read request 110 of theplurality of read requests, generates received read request eventinformation 108, and stores the received read request event information108 in the event memory 102 of the DS unit 36. The DS unit 36 processesthe read request 110 to retrieve a slice, generates slice retrievalprocessing event information 108, and stores the slice retrievalprocessing event information 108 in the event memory 102 of the DS unit36. The DS unit 36 sends a read response that includes the retrievedslice to DS processing unit 16, generates read response eventinformation 108, and stores the read response event information 108 inthe event memory of the DS unit 36.

Continuing with the example of operation, the DS processing unit 16receives a read response from each DS unit 36 of the plurality of DSunits 36 to produce a plurality of slices 1-5. The DS processing unit 16generates received read response event information 108 corresponding toeach of the received slices 1-5 and stores the read response eventinformation 108 in the event memory 102 of the DS processing unit 16.The DS processing unit 16 processes the received slices 1-5 by decodingthem to reproduce data 106. The DS processing unit 16 generates decodingevent information 108 and stores the decoding event information 108 inthe event memory 102 of the DS processing unit 16. The DS processingunit 16 sends a retrieval response that includes the data 106 to theuser device 12, generates retrieval response event information, andstores the retrieval response event information in the event memory ofthe DS processing unit 16. The user device 12 receives the retrievalresponse, generates received data event information 108, and stores thereceived data event information 108 in the event memory 102 associatedwith the user device 12.

Further continuing with the example of operation, each of the userdevice 12, the DS processing unit 16, and the plurality of DS units 36send event information 108 to the DS managing unit 18. The DS managingunit 18 collects and stores event information 108 in the event memory102 of the DS managing unit 18. The DS managing unit 18 receives ananalysis query from a requesting entity, retrieves event information 108from the event memory 102 of DS managing unit 18, aggregates the eventinformation 108, analyzes the event information 108 in accordance withthe analysis query to produce an analysis, generates a representation ofthe analysis, and sends the representation to the requesting entity.

FIG. 7A is a diagram illustrating an example of an event record 112 thatincludes a reporting entity identifier (ID) field 114, an event ID field116, a parent event ID field 118, and a table including a step field120, a timestamp field 122, a sequence number field 124, a messagesource field 126, and a other field 128. The event record 112 may beassociated with an event record ID. The reporting entity ID field 114includes a reporting entity ID entry signifying an entity generating theevent record 112. For example, reporting entity ID field 114 includes anentry of ID=2 when the reporting entity is DS unit 2. The event ID field116 includes an event ID entry signifying an ID of a common eventsubsequently utilized to correlate event information from two or morereporting entities. For example, DS unit 2 and user device 3 create anevent record utilizing event ID=54 when DS unit 2 and user device 3perform steps associated with a common transaction. The parent event IDfield 118 includes a parent event ID entry signifying an ID of an eventthat initialized the present event. For example, DS unit 4 receives aread request of event ID=20 and spawns a new event ID=21 to authenticatethe read request. The DS unit 4 generates an event record for eventID=21 that includes a parent event ID=20. Newly spawned events may spawneven more children events resulting in multiple layers of events.Multiple layers of events are discussed in greater detail with referenceto FIG. 8 .

The step field 120 includes a step entry describing a step of the event.For example, the step may be a received request step, a processing step,a send response step, an authentication step, etc. The timestamp field122 includes a timestamp entry associated with the step. The sequencenumber field 124 includes a sequence number entry of a protocol messagebetween two or more elements of a computing system. The message sourcefield 126 includes a message source entry identifier of a system elementsending an associated message. The other field 128 includes anotherentry for additional information associated with the step. The otherentry includes one or more of a slice name, a source name, a transactionnumber, a system element hardware ID, a software version number, asoftware pointer, a log record ID, and a statistics record ID.

FIG. 7B is a diagram illustrating an example of a log record 130 thatincludes a reporting entity identifier (ID) field 114, a table includinga state field 132, a timestamp field 122, a state descriptor field 134,and a state parameters field 136. The log record 130 may be associatedwith a log record ID. The state field 132 includes a state entrysignifying a state of one or more of a software process, an event, asystem state, a transaction, and a sequence. The state descriptor field134 includes a state descriptor entry qualitatively describing anassociated state. For example, request received, request process,response sent, authentication request, authentication approved, memoryavailable, system error, etc. The state parameters field 136 includes astate parameter entry signifying additional information associated withthe associated state. For example, a slice name, a slice size indicator,a sequence number, a transaction number, a software line, a softwarebreakpoint indicator, a message ID, a requester Internet protocoladdress, etc.

FIG. 7C is a diagram illustrating an example of a statistics record 138that includes a reporting entity identifier (ID) field 114 and a tableincluding a step field 120, a timestamp field 122, and one or morequantified descriptor fields 1-Q. Each quantified descriptor fieldincludes a type field 140 and a value field 142. The statistics record138 may be associated with a statistics record ID. The type field 140includes a type entry describing an associate value type. For example, anumber of errors, a loading factor, a bandwidth utilization factor, amemory utilization factor, a reliability indicator, and availabilityindicator, a queue depth indicator, a bandwidth indicator, a cacheavailability indicator, a data rate indicator, etc. The value field 142includes a quantitative value entry of the associated type of quantifieddescriptor.

FIG. 8 is a diagram illustrating an example of a hierarchal event recordrepresentation that includes a time sequential representation of anevent. The time sequential representation includes one or more childrenevents associated with the event. A child event of the one or morechildren events associated with the event is one layer removed from theevent and is represented by one indentation from the event towards theright. A child event may include one or more children events (e.g.,grandchildren) associated with the child event. A grandchild event ofthe child event is two layers removed from the event and is representedby two indentations from the event towards the right. A still furtherchild event may be at any layer removed from the event.

The representation may include a plurality of event information records(e.g., one or more of an event record, a log record, a statisticsrecord), wherein each event record includes at least an event identifier(ID) and a parent event ID when the event ID of the event record isassociated with a child event. For example, a read object event 144associated with a dispersed storage (DS) processing unit is assignedevent ID=101 is associated with a plurality of children events includinga received data retrieval request 146 event ID=102, a read 148 eventID=103, a decode 150 event ID=114, and a send data retrieval response154 event ID=116. Each child event of the plurality of children eventsis associated with parent event ID=101.

Children events may include further children events. For example, theread 148 event ID=103 includes read request 1-5 children eventsIDs=104-108 and read response 1-5 events IDs 109-113 when a response isreceived from five dispersed storage (DS) units. As another example, theread 148 event ID=103 includes read request 1-5 children eventsIDs=104-108 and read response 1-3 events IDs 109-111 when a response isreceived from three of five DS units. A subsequent analysis of therepresentation indicates that two slices were not received when thereceived response event includes read responses 1-3 (e.g., missingresponses 4-5). As another child of child event example, the decode 150event ID=114 includes a decode processing event ID=115. The read object144 event 101 finishes with the send data retrieval response 154 eventID=116.

FIG. 9A is a flowchart illustrating an example of generating eventrecords. The method begins at step 160 where a processing module of adevice affiliated with a dispersed storage network (DSN) sends an eventrequest that identifies an event to a dispersed storage (DS) processingmodule. The event includes a user access operation or a systemadministrative operation. The user access operation includes a varietyof operations such as a write operation, a read operation, and a deleteoperation. The system administrative operation includes a variety ofoperations such as a list operation, a list digest operation, and a scanfor slice errors operation.

The method continues at step 162 where the device generates an eventrecord regarding the event. The event record includes identity of thedevice, an event identifier (ID) associated with the event informationregarding initiation of the event, and information regarding completionof the event. The generating includes generating the event ID based onone or more of a random number, a previous event ID, a retrieved eventID, and a received event ID (e.g., from a management device) in responseto sending a query. The information regarding initiation of event andthe information regarding completion event includes one or more of atimestamp, a step descriptor, a sequence number, a message source ID, astate descriptor, an operation type, and an associated error message ID.For example, the device generates the information regarding initiationof the event to include a step descriptor corresponding to sending aread object request and generates the information regarding completionof the event to include a step descriptor corresponding to receiving adata object in response to the read object request.

The method continues at step 164 where the DS processing moduleprocesses the event request to produce a plurality of sub-eventrequests. For example, the DS processing module produces a plurality ofsets of read slice requests when the event request is a read data objectrequest. The method continues at step 166 where the DS processing modulegenerates a record regarding the processing of the event request. Therecord regarding the processing of the event request includes identityof the DS processing module, an event identifier (ID) associated withthe processing of the event request (e.g., a newly generated uniqueevent ID), a parent event ID associated with the event (e.g., the eventID associated with the event of the event request), informationregarding initiation of the processing of the event request, andinformation regarding completion of the processing of the event request.

The information regarding initiation of the processing of the eventrequest and the information regarding completion of the processing ofthe event request includes one or more of a timestamp, a stepdescriptor, a sequence number, a message source ID, a state descriptor,an operation type, and an associated error message ID. For example, theDS processing module generates the information regarding initiation ofthe processing of the event request to include a step descriptorcorresponding to receiving the read object request and a step descriptorcorresponding to outputting the plurality of sub-event requests. Asanother example, the DS processing module generates the informationregarding completion of the processing of the event requests to includea step descriptor corresponding to receiving decode threshold number ofencoded data slices, a step descriptor corresponding to decoding thedecode threshold number of encoded data slices to produce a datasegment, a step descriptor corresponding to aggregating a plurality ofdata segments, and a step descriptor corresponding to outputting a dataobject to the device.

The method continues at step 168 where the DS processing module sendsthe plurality of sub-event requests to a plurality of DS units of theDSN. The sending includes identifying the plurality of DS units. Forexample, the DS processing module selects a storage set of DS units forstorage of a plurality of sets of encoded data slices and outputs theplurality of sub-event requests to the storage set of DS units.

The method continues at step 170 where the plurality of DS unitsgenerates a plurality of records regarding processing of the pluralityof sub-event requests. A record of the plurality of records regardingthe processing of the plurality of sub-event requests includes identityof one of the plurality of DS units, an event identifier (ID) associatedwith the processing of a corresponding one of the plurality of sub-eventrequests by the one of the plurality of DS units (e.g., a newlygenerated unique event ID), a parent event ID associated with the eventrequest (e.g., the event ID of the event request), information regardinginitiation of the processing of the corresponding one of the pluralityof sub-event requests, and information regarding completion of theprocessing of the corresponding one of the plurality of sub-eventrequests. For example, a DS unit of the plurality of DS units generatesa record to include information regarding initiation of the processingincluding a step descriptor corresponding to receiving the correspondingone of the plurality of sub-event requests and a step descriptorcorresponding to retrieving an encoded data slice from memory of the DSunit when the corresponding one of the plurality of sub-event requestsincludes a read slice request. As another example, the DS unit of theplurality of DS units generates a record to include informationregarding completion of the processing including a step descriptorcorresponding to validating the retrieved encoded data slice to producea validated encoded data slice and a step descriptor corresponding tosending the validated encoded data slice to the DS processing module.

The method continues at step 172 where a management device affiliatedwith the DSN collects the event record, the record regarding theprocessing of the event request, and the plurality of records regardingthe processing of the plurality of sub-event requests to produce acollection of records. The collecting includes at least one ofgenerating and sending a record request to one or more of the DSprocessing module and the plurality of DS units, receiving records fromone or more of the DS processing module and the plurality of DS units,and retrieving the records from a local memory (e.g., retrieving arecords file that includes previously received records).

The method continues at step 174 where the management device evaluatesthe collection of records to produce performance information regardingthe DSN. The evaluating includes at least one of a performance responsetime of the DS processing unit and of one or more of the plurality of DSunits, performance reliability of the DS processing unit and of the oneor more of the plurality of DS units, and accessibility of the DSprocessing unit and of one or more of the plurality of DS units. Forexample, the management device calculates a difference between atimestamp of a record of information regarding initiation of theprocessing of the corresponding one of the plurality of sub-eventrequests and a timestamp of a record of information regarding completionof the processing of the corresponding one of the plurality of sub-eventrequests to produce a performance response time of the DS processingunit.

The method continues at step 176 where the management device collects aplurality of event records regarding a plurality of events, a pluralityof records regarding processing of a plurality of event requests, and aplurality of sets of records regarding processing of sets of sub-eventrequests. A set of the sets of records is regarding the processing of aset of sub-event requests of one of the plurality of event requests. Themethod continues at step 178 where the management device aggregates theplurality of event records, the plurality of records regardingprocessing of the plurality of event requests, and the plurality of setsof records regarding processing the sets of sub-event requests toproduce a plurality of collection of records.

FIG. 9B is a schematic block diagram of another embodiment of acomputing system that includes a device 180, a computing device 182, adispersed storage (DS) processing module 34, and a plurality of DS units36 of a dispersed storage network (DSN). The device 180 includes atleast one of a user device 12, a user device 14, a DS processing unit16, a storage integrity processing unit 20, a DS managing unit 18, and amanagement device affiliated with the DSN. The computing device 182 maybe utilized to implement at least one of the DS managing unit 18 and themanagement device. The computing device 182 includes a dispersed storagemodule 184. The DS module 184 includes a collect records module 186 andan evaluate records module 188.

The device 180 initiates an event by sending an event request 190 to theDS processing module 34. The event includes a user access operation or asystem administration operation. The device 180 generates an eventrecord 192 including information regarding the event. The event record192 includes identity of the device 180, an event identifier (ID)associated with the event, information regarding initiation of theevent, and information regarding completion of the event.

The DS processing module 34 processes the event request 190 to produce aplurality of sub-event requests 194. The DS processing module 34generates a record 196 regarding processing of event request includinginformation regarding the DS processing module 34 processing the eventrequest 190. The record 196 regarding the processing of the eventrequest 190 includes identity of the DS processing module 34, an eventidentifier (ID) associated with the processing of the event request 190,a parent event ID associated with the event, information regardinginitiation of the processing of the event request 190, and informationregarding completion of the processing of the event request 190.

The plurality of DS units 36 generates a plurality of records 198regarding processing of the plurality of sub-event requests 194including information regarding the plurality of DS units 36 of the DSNprocessing the plurality of sub-event requests 194. A record 198 of theplurality of records 198 regarding the processing of the plurality ofsub-event requests 194 includes identity of one of the plurality of DSunits 36, an event identifier (ID) associated with the processing of acorresponding one of the plurality of sub-event requests 194 by the oneof the plurality of DS units 36, a parent event ID associated with theevent request 190, information regarding initiation of the processing ofthe corresponding one of the plurality of sub-event requests 194, andinformation regarding completion of the processing of the correspondingone of the plurality of sub-event requests 194.

The collect records module 186 collects the event record 192, the record196 regarding processing of the event request 190, and the plurality ofrecords 198 regarding processing of the plurality of sub-event requests194 to produce a collection of records 200. The collecting includesreceiving a record in an unsolicited fashion, initiating a record query,and accessing the collection of records from a memory.

The evaluate records module 188 evaluates the collection of records 200to produce performance information 202 regarding the DSN. The evaluaterecords module 188 functions to evaluate by at least one of aperformance response time of the DS processing module 34 and of one ormore of the plurality of DS units 36, performance reliability of the DSprocessing module 34 and of the one or more of the plurality of DS units36, and accessibility of the DS processing module 34 and of one or moreof the plurality of DS units 36.

The collect records module 186 further functions to collect a pluralityof event records 192 regarding a plurality of events, a plurality ofrecords 196 regarding processing of a plurality of event requests 190,and a plurality of sets of records 198 regarding processing of sets ofsub-event requests 194, wherein a set of the sets of records isregarding the processing of a set of sub-event requests 194 of one ofthe plurality of event requests 190. The collect records module 186further functions to aggregate the plurality of event records 192, theplurality of records 196 regarding processing of the plurality of eventrequests 190, and the plurality of sets of records 198 regardingprocessing the sets of sub-event requests 194 to produce a plurality ofcollection of records.

FIG. 9C is a flowchart illustrating an example of collecting eventrecords. The method begins at step 204 where a processing module (e.g.,of a management device affiliated with a dispersed storage network(DSN)) collects an event record, a record regarding processing of anevent request, and a plurality of records regarding processing of aplurality of sub-event requests to produce a collection of records. Theevent record includes information regarding an event, wherein the eventis a user access operation or a system administrative operationinitiated by a device affiliated with the DSN. The record regardingprocessing of the event request includes information regarding adispersed storage (DS) processing module of the DSN processing the eventrequest to produce the plurality of sub-event requests. The plurality ofrecords regarding processing of the plurality of sub-event requestsincludes information regarding a plurality of DS units of the DSNprocessing the plurality of sub-event requests.

The method continues at step 206 where the processing module evaluatesthe collection of records to produce performance information regardingthe DSN. The evaluating includes at least one of a performance responsetime of the DS processing unit and of one or more of the plurality of DSunits, performance reliability of the DS processing unit and of the oneor more of the plurality of DS units, and accessibility of the DSprocessing unit and of one or more of the plurality of DS units.

The method continues at step 208 where the processing module collects aplurality of event records regarding a plurality of events, a pluralityof records regarding processing of a plurality of event requests, and aplurality of sets of records regarding processing of sets of sub-eventrequests, wherein a set of the sets of records is regarding theprocessing of a set of sub-event requests of one of the plurality ofevent requests. The method continues at step 210 where the processingmodule aggregates the plurality of event records, the plurality ofrecords regarding processing of the plurality of event requests, and theplurality of sets of records regarding processing the sets of sub-eventrequests to produce a plurality of collection of records.

FIG. 10A is a flowchart illustrating an example of generating a logrecord. The method begins with step 212 where a processing module (e.g.,of a reporting entity processing module) detects a state change. Thedetection may be based on one or more of a software flag, a message, apredetermination, a process output, a pattern match, a valid statetable, and a previous state condition. The method continues at step 214where the processing module obtains a state descriptor. The obtainingmay be based on one or more of the state change, lookup, generating astate descriptor, a state descriptor table lookup, retrieving the statedescriptor, and receiving the state descriptor in response to sending aquery. The method continues at step 216 where the processing moduleobtains a timestamp. The obtaining includes at least one of querying atime module, receiving the timestamp, and retrieving the timestamp.

The method continues at step 218 where the processing module obtainsstate parameters. The obtaining may be based on one or more of the statechange, the state descriptor, the timestamp, a state parameters tablelookup, retrieving state parameters, an error message, a parameter tablelookup, a parameter history record lookup, and receiving a stateparameter in response to sending a query. The method continues at step220 where the processing module generates a log record entry. Thegeneration includes aggregating the state change, the state descriptor,the timestamp, and the state parameters to produce entries for fields ofthe log record entry. The method continues at step 222 where theprocessing module facilitates storing the log record entry. Thefacilitation includes at least one of storing the log record entrylocally and sending the log record entry to another system element(e.g., a dispersed storage (DS) managing unit) for storage.

FIG. 10B is a flowchart illustrating an example of generating astatistics record, which includes similar steps to FIG. 10A. The methodbegins at step 224 where a processing module (e.g., of a reportingentity processing module) determines to create a statistics recordentry. The determination may be based on one or more of an errormessage, a state change, a time period, an event, a software flag, amessage, a predetermination, a process output, a pattern match, aprevious state condition, a previous statistic, a statisticalcorrelation output, and a request. For example, the processing moduledetermines to create the statistics record entry when a slice storageerror message is received.

The method continues at step 226 where the processing module obtains oneor more quantitative descriptor types. The obtaining may be based on oneor more of a lookup, a system condition, configuration information, aretrieval, a query, receiving, an error message, and a memoryutilization indicator. For example, the processing module obtains one ormore quantitative descriptor types from a table lookup based on theslice storage error message, wherein the one or more quantitativedescriptor types include available memory and memory device status.

The method continues at step 228 where the processing module obtains aquantitative descriptor value for each of the one or more quantitativedescriptor types. The obtaining may be based on one or more of a query,receiving, an error message, a historical record lookup, anotherstatistics record entry, and a value source indicator table lookup. Forexample, the processing module queries memory devices of a dispersedstorage (DS) unit and receives memory device status and available memoryinformation. The method continues with step 216 of FIG. 10A where theprocessing module obtains a timestamp. The method continues at step 232where the processing module generates a statistics record entry. Thegeneration includes generating one or more entries of fields of thestatistics record entry including a reporting entity identifier (ID), astep of a process and/or event, the timestamp, the one or morequantitative descriptor types, and one or more quantitative descriptorvalues corresponding to each of the one more quantitative descriptortypes. The method continues at 234 where the processing modulefacilitates storing the statistics record entry (e.g., storing locallyor sending the statistics record entry).

FIG. 10C is a flowchart illustrating an example of correlating astatistics record and a log record. The method begins at step 236 wherea processing module (e.g., of a reporting entity, a dispersed storage(DS) managing unit) determines whether to create an error report. Thedetermination may be based on one or more of a report time periodexpiration, an error message, a request, a predetermination, a softwaretrigger, a state transition detection, and a quantified descriptor valuecompares unfavorably to a descriptor threshold. For example, theprocessing module determines to create the error report when a reporttime period has expired since generation of a previous error report anda store slice error message has been received. The method continues tostep 238 when the processing module determines to create the errorreport. The method continues at step 238 where the processing modulecorrelates one or more statistics record entries with one or more logrecord entries to produce the error report. The correlation may be basedon one more of similar timestamps, similar reporting identifiers (IDs),similar event IDs, records with similar parent IDs, and recordsassociated with a similar level of a hierarchical representation of anevent sequence.

FIG. 11 is a flowchart illustrating an example of generating arepresentation of event records. The method begins at step 240 where aprocessing module (e.g., of a dispersed storage (DS) managing unit)captures event information, creates event records, and stores the eventrecords as previously described. The method continues at step 242 wherethe processing module receives an event representation request (e.g.,from a user device, from another DS managing unit, from an errorcorrelation process). The representation request includes one or morequery filters, wherein a query filter of the query filters includes oneor more of a request type, a level indicator, a reporting entityidentifier (ID), an event ID, a parent event ID, and a child event ID.

The method continues at step 244 where the processing module identifiesevent record entries based on the representation request. Theidentification includes searching event record entries by comparing aquery filter to an event record entry to identify favorable comparisons.The method continues at step 246 where the processing module obtains theidentified event record entries. The obtaining includes retrieving theidentified event record entries from one or more reporting entitiesstoring event records. For example, the processing module determines toobtain the identified event record entries from a set of DS units andsends an event record entry retrieval request to each DS unit of a setof DS units. Next, the processing module receives retrieval responsesfrom each DS unit of a set of DS units that includes the identifiedevent record entries. The method continues at step 248 where theprocessing module generates a representation of the identified eventrecord entries.

The generation includes at least one of sorting by time, sorting byevent ID, sorting by parent/child relationship, sorting by event level,displaying in a hierarchical view of parent events and child events(e.g., as described with reference to FIG. 8 ). The method continues atstep 250 where the processing module outputs a representation to arequesting entity.

FIG. 12 is a flowchart illustrating an example of analyzing eventrecords, which include similar steps to FIG. 11 . The method begins withstep 240 of FIG. 11 where a processing module (e.g., of a dispersedstorage (DS) managing unit) captures event information, creates eventrecords, and stores the event records. The method continues at step 254where the processing module receives an event analysis query (e.g., froma user device, from another DS managing unit, from an error correlationprocess). The event analysis query includes one or more event queryfilters, wherein an event query filter of the event query filtersincludes one or more of a request type, a requester identifier (ID), alevel indicator, a reporting entity identifier (ID), an event ID, aparent event ID, and a child event ID.

The method continues at step 256 where the processing module identifiesa sequence number and reporting entities associated with the query. Theidentification includes at least one of identifying a common sequencenumber of two or more event record entries associated with the query,identifying a requester ID from the two or more event record entries,and identifying a receiver ID from the two or more event record entries.For example, the processing module identifies a sequence numberassociated with a read request event and a read response event of thequery. As another example, the processing module identifies these units1-5 as reporting entities associated with the query.

The method continues at step 258 where the processing module identifiesevent record entries based on the sequence number and the entitiesassociated with the query. The identification may be based on one ormore of event record entries associated with the sequence number (e.g.,an event record entry contains the sequence number) and event recordentries associated with the entities associated with the query. Themethod continues with steps 246-250 of FIG. 11 where the processingmodule obtains the identified event record entries, generates arepresentation of the identified event record entries, and outputs therepresentation to a requesting entity.

FIG. 13 is a flowchart illustrating an example of combining adjunctinformation with event records, which include similar steps to FIG. 11 .The method begins with step 240 of FIG. 11 where a processing module(e.g. of a reporting entity), captures event information, creates eventrecords, and stores the event records. The method continues at step 268where the processing module receives adjunct event information. Theadjunct information includes at least one of a self-monitoring, analysisand reporting technology (SMART) log; a syslog; Java garbage collectors;a wrapper script; a test script; and a machine reboot indicator. Thereceiving includes at least one of receiving the adjunct eventinformation from an external device in response to a query, monitoringthe external device, and receiving a message.

The method continues at step 270 where the processing module determinesan event information record format based on the adjunct eventinformation. The event information record format includes at least oneof an event record format, a log record format, and a statistics recordformat. The determination may be based on one or more of a format tablelookup, a query, a message, and the adjunct information. The methodcontinues at step 272 where the processing module generates an eventinformation record entry based on the adjunct information in accordancewith the event information record format. For example, the processingmodule generates a log record to include at least a portion of theadjunct event information when the event information record format isthe log format. The method continues at step 274 where the processingmodule facilitates storing event information record (e.g., storinglocally, sending).

FIG. 14 is a flowchart illustrating another example of analyzing eventrecords, which include similar steps to FIGS. 11-12 . The method beginswith step 240 of FIG. 11 where a processing module (e.g. of a dispersedstorage (DS) processing unit) captures event information, creates eventrecords, and stores the event records. The method continues with step254 of FIG. 12 where the processing module receives an event analysisquery. The method continues at step 280 where the processing moduledetermines an event filter based on the query. The determination may bebased on at least one of a query type of the event analysis query and aquery filter table lookup utilizing the query type is an index. Multipleevent filters may be required to provide a favorable search.

The method continues at step 282 where the processing module determineswhether another event filter is required. The determination may be basedon one or more of a current member filters, an estimated number ofrequired filters, a query filter table lookup to determine and estimatednumber of required filters, a message, and a search test resultutilizing a current member of event filters. The method repeats back tostep 280 when the processing module determines that another event filteris required. The method continues to step 244 of FIG. 11 when theprocessing module determines that another event filter is not required.

The method continues with steps 244-250 of FIG. 11 where the processingmodule identifies event record entries based on the request, obtains theidentified event record entries, generates a representation of theidentified event record entries, and outputs the representation to arequesting entity when the processing module determines that anotherevent filter is not required. The method continues at step 292 where theprocessing module determines whether to initiate corrective action basedon the representation. The determination may be based on one or more ofa comparison of at least a portion of the representation to arepresentation threshold, a comparison of at least a portion of therepresentation to a previous representation identifying a trend, anddetermining that a comparison is unfavorable. For example, theprocessing module determines to initiate corrective action when therepresentation indicates that a decode threshold number of dispersedstorage units are not available within a set of DS units utilize tostore a plurality of sets of encoded data slices.

The method branches to step 296 when the processing module determines toinitiate corrective action. The method ends at step 294 when theprocessing module determines to not initiate corrective action. Themethod continues at step 296 where the processing module initiatescorrective action based on the representation. The corrective actionincludes at least one of rebuilding a slice, disabling a DS unit,enabling a hot standby DS unit, migrating a slice, sending an errormessage, and blocking a request. For example, the processing moduleinitiates corrective action to include enabling the hot standby DS unitand migrating slices from a failing DS unit to the hot standby DS unitwhen the representation indicates that the failing DS unit hasunacceptable performance.

FIG. 15A is a schematic block diagram of another embodiment of acomputing system that includes dispersed storage network (DSN) 22 and acomputing device 300. The DSN 22 includes a device 180, a dispersedstorage (DS) processing module 34, a plurality of DS units 36, andalternatively may include the computing device 300. The device 180includes at least one of a user device 12, a user device 14, a DSprocessing unit 16, a storage integrity processing unit 20, a DSmanaging unit 18, and a management device affiliated with the DSN 22.The DS processing unit 34 may be implemented in one or more of the userdevice 12, the DS processing unit 16, and a DS unit 36. The computingdevice 300 may be utilized to implement at least one of the DS managingunit 18 and the management device. The computing device 300 includes adispersed storage (DS) module 302. The DS module 302 includes a selecterror messages module 304, an identify records module 306, an identifycause module 308, and a generate representation module 310.

The select error messages module 304 selects two or more correlatederror messages 312 of a plurality of error messages 314 from the DSN 22.An error message of the plurality of error messages 314 includes atleast one of an object identifier (ID), a data ID, a reporting entityID, a timestamp, an error type indicator, and an event ID. The selectingincludes receiving the plurality of error messages 314. Receiving theerror message includes at least one of receiving an unsolicited errormessage, receiving the error message in response to a query, andretrieving the error message from an error message list. The selecterror messages module 304 selects the two or more correlated errormessages 312 by selecting a first one of the two or more correlatederror messages based on an error selection scheme (e.g., a random errormessage, for a selected reporting entity, for a selected timeframe, foran error type, for a DSN address).

The select error messages module 304 identifies remaining ones of thetwo or more correlated error messages in a variety of ways. In a firstidentification method, the select error messages module 304 identifiesthe remaining ones of the two or more correlated error messages thatincludes an error type in common with the first one of the two or moreerror messages (e.g., errors of same type, like a corrupted slicemessage). In a second identification method, the select error messagesmodule 304 identifies the remaining ones of the two or more correlatederror messages that includes a reporting entity in common with the firstone of the two or more correlated error messages (e.g., errors from samesource). In a third identification method, the select error messagesmodule 304 identifies the remaining ones of the two or more correlatederror messages that includes a timestamp value correlating with atimestamp of the first one of the two or more correlated error messages(e.g., errors occurred at about the same time). In a fourthidentification method, the select error messages module 304 identifiesthe remaining ones of the two or more correlated error messages thatincludes an operation type in common with the first one of the two ormore correlated error messages (e.g., errors when same operationperformed such as retrieving). In a fifth identification method, theselect error messages module 304 identifies the remaining ones of thetwo or more correlated error messages that includes DSN addressinginformation in common with the first one of the two or more correlatederror messages (e.g., errors retrieving same slice).

The identify records module 306 identifies two or more collection ofrecords 314 corresponding to the selected two or more correlated errormessages 312. A collection of records of the two or more collection ofrecords 314 includes a variety of records received from the DSN 22. Thevariety of records includes an event record 192 including informationregarding an event, a first record 196 (e.g., an event requestprocessing record) including information regarding the DS processingmodule 34 processing an event request 190 to produce a plurality ofsub-event requests 194, and a plurality of records 198 includinginformation regarding the plurality of DS units 36 processing theplurality of sub-event requests 194. The event is a user accessoperation or a system administrative operation initiated by the device180 affiliated with the DSN 22. The event request 190 is regarding theevent.

The identify records module 306 functions to identify a first of the twoor more collection of records by identifying a first event correspondingto a first one of the two or more correlated error messages, determininga parent event identifier for the first event, and identifying a firstone of the two or more collection of records based on the parent eventidentifier. The identifying the first event includes at least one ofidentifying a timestamp that compares favorably to a timestamp of thefirst one of the two or more correlated error messages and extracting afirst event record ID from the first correlated error message. The eventrecord includes identity of the device 180 that initiated the event, anevent ID associated with the event, information regarding initiation ofthe event, and information regarding completion of the event.

The first record includes identity of the DS processing module 34, anevent identifier (ID) associated with the processing of the eventrequest 190, a parent event ID associated with the event, informationregarding initiation of the processing of the event request 190, andinformation regarding completion of the processing of the event request190. One of the plurality of records 198 includes identity of one of theplurality of DS units 36, an event identifier (ID) associated with theprocessing of a corresponding one of the plurality of sub-event requests194 by the one of the plurality of DS units 36, a parent event IDassociated with the event request 190, information regarding initiationof the processing of the corresponding one of the plurality of sub-eventrequests 194, and information regarding completion of the processing ofthe corresponding one of the plurality of sub-event requests 194.

The identify cause module 308 identifies a correlation cause 316 of oneor more errors corresponding to the two or more correlated errormessages 312 based on the two or more collections of records 314. Theidentify cause module 308 functions to identify the correlation cause316 by one or more of a variety of methods. In a first method, theidentify cause module 308 identifies the correlation cause 316 as one ofthe plurality of DS units 36 failing to perform a function correspondingto the processing of one of the plurality of sub-event requests 194(e.g., a missing step compared to each other, compared to a template foran event type). In a second method, the identify cause module 308identifies the correlation cause 316 as the DS processing unit 36failing to perform a function corresponding to the processing of theevent request 194. In a third method, the identify cause module 308identifies the correlation cause 316 as one of the plurality of DS units36 performing an additional function in excess of the processing of theone of the plurality of sub-event requests 194 (e.g., extra compared toeach other, compared to a template for an event type).

In a fourth method, the identify cause module 308 identifies thecorrelation cause 316 as the DS processing unit 36 performing anadditional function in excess of the processing of the event request194. In a fifth method, the identify cause module 308 identifies thecorrelation cause 316 as one of the plurality of DS units 36 or the DSprocessing module 34 performing a function of the respective processingout of order of an expected function processing sequence (e.g., out oforder compared to each other, compared to a template for an event type).In a sixth method, the identify cause module 308 identifies thecorrelation cause 316 as one of the plurality of DS units 36 failing toperform the function corresponding to the processing of one of theplurality of sub-event requests 194 within an expected time frame (e.g.,late compared to each other, late compared to a template for an eventtype, early). The generate representation module 310 generates agraphical representation of at least one of the two or more collectionof records 314 to illustrate the correlation cause 316. For example, thegenerate representation module 314 generates a graphical representationas illustrated in FIG. 8 .

FIG. 15B is a flowchart illustrating an example of determining a causeof an error within a dispersed storage network (DSN). The method beginsat step 320 where a processing module (e.g., of a dispersed storage (DS)managing unit) selects two or more correlated error messages of aplurality of error messages. The selecting the two or more correlatederror messages includes selecting a first one of the two or morecorrelated error messages based on an error selection scheme (e.g., byreporting entity, by error type, by timestamp, randomly, by a paretochart, etc.) and identifying the remaining ones of the two or morecorrelated error messages by one or more of a variety of methods. Afirst method includes identifying remaining ones of the two or morecorrelated error messages that includes an error type in common with thefirst one of the two or more error messages (e.g., errors of same typesuch as a corrupted slice message). A second method includes identifyingremaining ones of the two or more correlated error messages thatincludes a reporting entity in common with the first one of the two ormore correlated error messages (e.g., errors from a common reportingentity).

A third method includes identifying remaining ones of the two or morecorrelated error messages that includes a timestamp value correlatingwith a timestamp of the first one of the two or more correlated errormessages (e.g., errors occurring within a time window of each other). Afourth method includes identifying remaining ones of the two or morecorrelated error messages that includes an operation type in common withthe first one of the two or more correlated error messages (e.g., errorswhen a common operation performed such as storing a slice of a commonslice name). A fifth method includes identifying remaining ones of thetwo or more correlated error messages that includes DSN addressinginformation in common with the first one of the two or more correlatederror messages (e.g., errors retrieving a common slice with a commonslice name).

The method continues at step 322 or a processing module identifies twoor more collection of records corresponding to the selected two or morecorrelated error messages. A collection of records of the two or morecollection of records includes an event record, a first record, and aplurality of records including information regarding a plurality of DSunits processing the plurality of sub-event requests. The event recordincludes information regarding an event. The event includes a useraccess operation or a system administrative operation initiated by adevice affiliated with the DSN. The event record further includesidentity of a device that initiated the event, an event identifier (ID)associated with the event, information regarding initiation of theevent, and information regarding completion of the event. The firstrecord includes information regarding a DS processing module processingan event request to produce a plurality of sub-event requests, whereinthe event request is regarding the event.

The first record further includes identity of the DS processing module,an event ID associated with the processing of the event request, aparent event ID associated with the event, information regardinginitiation of the processing of the event request, and informationregarding completion of the processing of the event request. One of theplurality of records includes identity of one of the plurality of DSunits, an event identifier (ID) associated with the processing of acorresponding one of the plurality of sub-event requests by the one ofthe plurality of DS units, a parent event ID associated with the eventrequest, information regarding initiation of the processing of thecorresponding one of the plurality of sub-event requests, andinformation regarding completion of the processing of the correspondingone of the plurality of sub-event requests.

The identifying the one of the two or more collection of recordsincludes a sequence of steps. In a first step, the processing moduleidentifies a first event corresponding to a first one of the two or morecorrelated error messages (e.g., timestamps fall within a common timeframe, an identifier of the first event matches an event identifier ofthe first correlated error message). In a second step, the processingmodule determines a parent event identifier for the first event (e.g.,via extracting the parent event identifier from a first record of thefirst event). In a third step, the processing module identifiers a firstone of the two or more collection of records based on the parent eventidentifier (e.g., event records sharing the parent identifier, eventrecords whose parents share the parent identifier, etc.).

The method continues at step 324 where the processing module identifiesa correlation cause of one or more errors corresponding to the two ormore correlated error messages based on the two or more collections ofrecords. The identifying the correlation cause includes at least one ofa variety of ways. In a first way, the processing module identifies thecorrelation cause as one of the plurality of DS units failing to performa function corresponding to the processing of one of the plurality ofsub-event requests. In a second way, the processing module identifiesthe correlation cause as the DS processing unit failing to perform afunction corresponding to the processing of the event request. In athird way, the processing module identifies the correlation cause as oneof the plurality of DS units performing an additional function in excessof the processing of the one of the plurality of sub-event requests.

In a fourth way, the processing module identifies the correlation causeas the DS processing unit performing an additional function in excess ofthe processing of the event request. In a fifth way, the processingmodule identifies the correlation cause as one of the plurality of DSunits or the DS processing unit performing a function of the respectiveprocessing out of order of an expected function processing sequence. Ina sixth way, the processing module identifies the correlation cause asone of the plurality of DS units failing to perform the functioncorresponding to the processing of one of the plurality of sub-eventrequests within an expected time frame. The method continues at step 326where the processing module generates a graphical representation of atleast one of the two or more collection of records to illustrate thecorrelation cause.

FIG. 16A is a schematic block diagram of another embodiment of acomputing system that includes a dispersed storage network (DSN) 22 anda computing device 330. The DSN 22 includes a device 180, a dispersedstorage (DS) processing module 34, a plurality of DS units 36, andalternatively may include the computing device 330. The device 180includes at least one of a user device 12, a user device 14, a DSprocessing unit 16, a storage integrity processing unit 20, a DSmanaging unit 18, and a management device affiliated with the DSN 22.The DS processing unit 34 may be implemented in one or more of the userdevice 12, the DS processing unit 16, and a DS unit 36. The computingdevice 330 may be utilized to implement at least one of the DS managingunit 18 and the management device. The computing device 330 includes adispersed storage module 332. The DS module 332 includes an identifycollections of records module 334, an identify error module 336, anidentify rebuilding module 338, a rebuilding module 340, and a rebuildslice module 342.

The identify collections of records module 334 identifies a set ofcollections of records 344 corresponding to a data segment that isstored in the DSN 22 as a set of encoded data slices. A collection ofrecords of the set of collections of records 344 includes a variety ofrecords received from the DSN 22. The variety of records includes anevent record 192 including information regarding an event, a firstrecord 196 (e.g., an event request processing record) includinginformation regarding the DS processing module 34 processing an eventrequest 190 to produce a plurality of sub-event requests 194, and aplurality of records 198 including information regarding the pluralityof DS units 36 processing the plurality of sub-event requests 194. Theevent is a user access operation or a system administrative operationinitiated by the device 180 affiliated with the DSN 22. The eventrequest 190 is regarding the event.

The first record includes identity of the DS processing module 34, anevent identifier (ID) associated with the processing of the eventrequest 190, a parent event ID associated with the event, informationregarding initiation of the processing of the event request 190, andinformation regarding completion of the processing of the event request190. One of the plurality of records 198 includes identity of one of theplurality of DS units 36, an event identifier (ID) associated with theprocessing of a corresponding one of the plurality of sub-event requests194 by the one of the set of DS units 36, a parent event ID associatedwith the event request 190, information regarding initiation of theprocessing of the corresponding one of the plurality of sub-eventrequests 194, and information regarding completion of the processing ofthe corresponding one of the plurality of sub-event requests 194.

The identify collections of records module 334 further functions toidentify the set of collections of records by selecting the data segmentbased on one or more of an error message 314 regarding the one of theset of encoded data slices (e.g., a corrupted slice error message), anerror of another data segment of a data object in common with the datasegment, a data segment analysis list (e.g., a next segment foranalysis) a random selection process, and a request to analyze the datasegment (e.g., a request from a DS managing unit 18). The identifycollections of records module 334 functions to identify the set ofcollections of records 344 by a series of steps. A first step includesidentifying events associated with the data segment. For one of theevents, a second step includes identifying the one of the eventscorresponding to a user access operation regarding the data segment(e.g., any writes to or reads associated with the data segment). A thirdstep includes determining a parent event identifier for the first event(e.g., via a records lookup). A fourth step includes identifying one ofthe set of the collections of records based on the parent eventidentifier (e.g., all levels of records that connect to a highest levelof the parent event identifier).

The identify error module 336 determines whether an error 346 exists forone of the set of encoded data slices based on at least some of the setof collections of records. The identify error module 336 functions todetermine whether the error 346 exists by at least one of severalmethods. A first method includes identifying a record of the set ofcollections of records 344 in which one of the set of DS units 36 failedto perform an expected function (e.g., missing a step such as no writeacknowledgment or no commit acknowledgment). A second method includesidentifying another record of the set of collections of records 344 inwhich the DS processing module 34 failed to perform another expectedfunction (e.g., not sending a sub-event request to a DS unit 36). Athird method includes identifying a group of records of the set ofcollections of records 344 from which an out-of-order processing of aset of expected functions is detected (e.g., sending a commitacknowledgment before a write acknowledgment). A fourth method includesidentifying yet another record of the set of collections of records 344in which the one of the set of DS units 36 failed to perform theexpected function within an expected time frame (e.g., sending a commitacknowledgment minutes after receiving a commit request, sending aretrieved slice minutes after receiving a retrieved slice request). Afifth method includes identifying an error message of the plurality oferror messages 314 and/or an error message pattern that comparesfavorably to a retrieved error message pattern of the error 346.

The identify rebuilding module 338, when the error 346 exists, flags theone of the set of encoded data slices for potential rebuilding 348. Theflagging includes at least one of generating a list of slices forpotential rebuilding, modifying the set of collections of records 344 toindicate slice names corresponding to the one of the set of encoded dataslices for potential rebuilding, and outputting the list of slices forpotential rebuilding to the storage integrity processing module 20.

The rebuilding module 340 determines that errors 346 exist for two ormore encoded data slices of the set of encoded data slices based on theat least some of the set of collections of records 344. The rebuildingmodule 340 determines whether to rebuild each of the two or more encodeddata slices (e.g., based on one or more of a system capacity indicator,a reliability level indicator, a reliability level goal, apredetermination, a request). When a determination is made to rebuildone of the two or more encoded data slices, the rebuilding module 340initiates rebuilding of the one of the two or more encoded data slicesand unflags remaining ones of the two or more of encoded data slices forpotential rebuilding. The initiating includes at least one of generatinga rebuild slice request 350 that includes a slice name of the one of thetwo or more encoded data slices, outputting the rebuild slice request350 to at least one of the storage integrity processing unit 20 and therebuild slice module 342, and directly rebuilding the one of the two ormore encoded data slices to produce a rebuilt slice version 354 forstorage in the DSN 22.

The rebuild slice module 342 rebuilds the one of the set of encoded dataslices by a series of steps. In a first step the rebuild slice module342 generates a decoding coded matrix of coded values of at least adecode threshold number of encoded data slices 352 of the set of encodeddata slices in accordance with a dispersed storage error codingfunction. For example, the rebuild slice module 342 generates thedecoding coded matrix to include a column of the coded values of the atleast the decode threshold number of encoded data slices 352. In asecond step the rebuild slice module 342 generates a data matrix fromthe decoding coded matrix and a decoding matrix in accordance with thedispersed storage error coding function, wherein the data matrixrepresents a rebuilding of the data segment. The decoding matrix isgenerated from a corresponding encoding matrix by eliminating rows ofthe encoding matrix for all rows except rows corresponding to the rowsof the decoding coded matrix to produce a reduced encoding matrix andinverting the reduced encoding matrix to produce the decoding matrix.The rebuild slice module 342 generates the data matrix by matrixmultiplying the decoding coded matrix by the decoding matrix.

In a third step the rebuild slice module 342 generates a rebuilt codedmatrix from the data matrix and the encoding matrix. For example, therebuild slice module 342 matrix multiplies the data matrix by theencoding matrix to produce the rebuilt coded matrix. In a fourth stepthe rebuild slice module 342 generates the rebuilt slice version 354 ofthe one of the set of encoded data slices from the rebuilt coded matrixfor storage in the DSN 22. For example, the rebuild slice module 342extracts the rebuilt slice version 354 from the rebuilt coded matrix.

FIG. 16B is a flowchart illustrating an example of identifying a sliceto rebuild. The method begins at step 360 where a processing module(e.g., a dispersed storage (DS) processing module of a DS managing unit)identifies a set of collections of records corresponding to a datasegment that is stored in a dispersed storage network (DSN) as a set ofencoded data slices. A collection of records of the set of collectionsof records includes an event record including information regarding anevent, a first record including information regarding a DS processingmodule processing an event request to produce a plurality of sub-eventrequests, and a plurality of records including information regarding aset of DS units processing the plurality of sub-event requests. Theevent is a user access operation or a system administrative operationinitiated by a device affiliated with the DSN. The event request isregarding the event.

The first record includes identity of the DS processing module, an eventidentifier (ID) associated with the processing of the event request, aparent event ID associated with the event, information regardinginitiation of the processing of the event request, and informationregarding completion of the processing of the event request. One of theplurality of records includes identity of one of the set of DS units, anevent identifier (ID) associated with the processing of a correspondingone of the plurality of sub-event requests by the one of the set of DSunits, a parent event ID associated with the event request, informationregarding initiation of the processing of the corresponding one of theplurality of sub-event requests, and information regarding completion ofthe processing of the corresponding one of the plurality of sub-eventrequests.

The identifying the set of collections of records further includesselecting the data segment based on one or more of an error messageregarding the one of the set of encoded data slices, an error of anotherdata segment of a data object in common with the data segment, a datasegment analysis list, a random selection process, and a request toanalyze the data segment. The identifying the set of collections ofrecords includes identifying events associated with the data segment andfor one of the events at least one of a variety of steps. A first stepincludes identifying the one of the events corresponding to a useraccess operation regarding the data segment. A second step includesdetermining a parent event identifier for the first event. A third stepincludes identifying one of the set of the collections of records basedon the parent event identifier.

The method continues at step 362 where the processing module determineswhether an error exists for one of the set of encoded data slices basedon at least some of the set of collections of records. The determiningwhether the error exists includes at least one of a variety of methods.In a first method, the processing module identifies a record of the setof collections of records in which one of the set of DS units failed toperform an expected function. In a second method, the processing moduleidentifies another record of the set of collections of records in whichthe DS processing module failed to perform another expected function. Ina third method, the processing module identifies a group of records ofthe set of collections of record from which an out-of-order processingof a set of expected functions is detected. In a fourth method, theprocessing module identifies yet another record of the set ofcollections of records in which the one of the set of DS units failed toperform the expected function within an expected time frame.

When the error exists, the method continues at step 364 where theprocessing module flags the one of the set of encoded data slices forpotential rebuilding. The method continues at step 366 where theprocessing module determines that errors exist for two or more encodeddata slices of the set of encoded data slices based on the at least someof the set of collections of records. The method continues at step 368where the processing module determines whether to rebuild each of thetwo or more encoded data slices. When a determination is made to rebuildone of the two or more encoded data slices, the method continues at step370 where the processing module unflags remaining ones of the two ormore of encoded data slices for potential rebuilding.

The method continues at step 372 where the processing module initiatesrebuilding of the one of the two or more encoded data slices. The methodcontinues at step 374 where the processing module generates a decodingcoded matrix of coded values of at least a decode threshold number ofencoded data slices of the set of encoded data slices in accordance witha dispersed storage error coding function. The method continues at step376 where the processing module generates a data matrix from thedecoding coded matrix and a decoding matrix in accordance with thedispersed storage error coding function, wherein the data matrixrepresents a rebuilding of the data segment. The method continues atstep 378 where the processing module generates a rebuilt coded matrixfrom the data matrix and an encoding matrix. The method continues atstep 380 where the processing module generates a rebuilt version of theone of the set of encoded data slices from the rebuilt coded matrix.

FIG. 17 is a flowchart illustrating an example of correlating errors,which include similar steps to FIG. 11 . The correlation and eventanalysis described with respect to FIG. 17 can be used to improvefailure prediction. Often, failures are preceded by one or moresuspicious but non-critical messages. Left alone, they are often missedby conventional systems. Event correlation, as described herein, cantake note of these warnings, and correlate them to predict disk,enclosure, and/or other types of failures before they occur. Asdiscussed below, the correlation an error prediction can includeobtaining logs and statistics around the timeframe of the event, andperforming a statistical analysis of the frequency of events whichhappen more frequently or less frequently, in the space or time close tothe failure event. Techniques used can include Bayesian analysis, Markovchains, neural networks and other techniques common for building expertsystems. The results of this analysis are then used to generate ananalysis engine which predicts likelihood of different classes offailure based on the results of the statistics and events coming intothe system.

The method illustrated in FIG. 17 begins with step 382 where aprocessing module (e.g., of a dispersed storage (DS) managing unit)receives an error message. The method continues at step 384 where theprocessing module determines an event identifier (ID) corresponding tothe error message. The method continues at step 386 where the processingmodule identifies event record entries preceding, including, andfollowing an event of the event ID. The identifying may be based on atleast one of a timestamp associated with event ID and event recordentries associated with a timestamp within a predetermined time beforeand after the timestamp. For example, the processing module identifiesevent record entries that are time stamped within 60 minutes of theevent ID when the predetermined time is 60 minutes. The method continuesat step 246 of FIG. 11 where the processing module obtains the eventrecord entries.

The method continues at step 390 where the processing module identifiesa previous associated error. The identification maybe based on one ormore of the error message and the identified event record entries. Theprevious associated error includes at least one of a previous error thatis substantially the same as an error of the error message and aprevious error that is related to (e.g., same error family, samereporting entity IDs, typically present with the error) the error of theerror message. The method continues at step 392 where the processingmodule determines a previous event ID corresponding to the previouserror (e.g., a lookup). The method continues at step 394 where theprocessing module identifies previous event record entries preceding,including, and following an event of the previous event ID. The methodcontinues at step 396 where the processing module obtains the previousevent record entries.

The method continues at step 398 where the processing module identifiesone or more correlated entries between the event record entries and theprevious event record entries. The identifying includes comparingentries to identify a favorable correlation trend. For example, theprocessing module compares entries to identify an event that alwaysprecedes the error. The method continues at step 400 where theprocessing module generates an error correlation report based on the oneor more correlated entries. The generation includes identifying andaggregating report elements including one or more of the error message,the one or more correlated entries, the event ID, the previousassociated error, the previous event ID, and a timestamp. The methodcontinues at step 402 where the processing module facilitates storingthe error correlation report. The facilitation includes at least one ofstoring the error correlation report in a local memory and sending theerror correlation report to another system entity (e.g., a requestingentity).

FIG. 18A is a schematic block diagram of another embodiment of acomputing system that includes a dispersed storage network (DSN) 22 anda computing device 410. The DSN 22 includes a device 180, a dispersedstorage (DS) processing module 34, a plurality of DS units 36, andalternatively may include the computing device 410. The device 180includes at least one of a user device 12, a user device 14, a DSprocessing unit 16, a storage integrity processing unit 20, a DSmanaging unit 18, and a management device affiliated with the DSN 22.The DS processing unit 34 may be implemented in one or more of the userdevice 12, the DS processing unit 16, and a DS unit 36. The computingdevice 410 may be utilized to implement at least one of the DS managingunit 18 and the management device. The computing device 410 includes adispersed storage module 412. The DS module 412 includes an identifyanomaly module 414, an identify set of collections of records module416, a determine significance module 418, and a modify records module420.

The identify anomaly module 414 identifies a performance anomaly 422within the DSN 22. The identifying includes receiving a plurality oferror messages 314 from the DSN 22 and may include accessing collectionsof records 424. The performance anomaly 422 includes one or more of anencoded data slice error, a DS module 34 processing error, a deviationfrom expected performance of a DS unit 36 of the set of DS units 36, adeviation from expected performance of the DS processing module 34, adeviation from an expected ordering of performance of functions, a delayin performance of an expected function, and an error in performance ofthe expected function. The identify anomaly module 414 functions toidentify the performance anomaly by a variety of approaches. In a firstapproach, the identify anomaly module 414 identifies a deviation ofperformance of one or more of the set of DS units 36 from an expected DSunit performance trend. For example, the identify anomaly module 414receives a collection of records 424 that identifies a DS unit 36 thatresponds more slowly to an excess request sequence than other DS units36 of the set of DS units 36. In a second approach, the identify anomalymodule 414 identifies another deviation of the DS processing module 34from an expected DS processing module performance trend.

The identify set of collections of records module 416 identifies a setof collections of records 424 corresponding to the performance anomaly422. The identifying may include receiving the plurality of errormessages 314 and receiving the performance anomaly 422. One of the setof collections of records includes an event record including informationregarding an event, a first record including information regarding theDS processing module 34 processing an event request to produce aplurality of sub-event requests, and a plurality of records includinginformation regarding the set of DS units 36 processing the plurality ofsub-event requests. The event is a user access operation or a systemadministrative operation initiated by a device affiliated with the DSN22. The event request is regarding the event.

The first record includes identity of the DS processing module 34, anevent identifier (ID) associated with the processing of the eventrequest, a parent event ID associated with the event, informationregarding initiation of the processing of the event request, andinformation regarding completion of the processing of the event request.One of the plurality of records includes identity of one of the set ofDS units 36, an event identifier (ID) associated with the processing ofa corresponding one of the plurality of sub-event requests by the one ofthe set of DS units 36, a parent event ID associated with the eventrequest, information regarding initiation of the processing of thecorresponding one of the plurality of sub-event requests, andinformation regarding completion of the processing of the correspondingone of the plurality of sub-event requests.

The identify set of collections of records module 416 functions toidentify the set of collections of records 426 by identifying eventsassociated with the performance anomaly 422 (e.g., identifying eventrecords of events that include a DSN address associated with theperformance anomaly and/or identifying event records of events thatcorrespond to a timeframe of the performance anomaly). For one of theevents, the identify set of collections of records module 416 identifiesthe one of the events corresponding to a user access operation regardingthe performance anomaly (e.g., any writes to or reads associated withthe performance anomaly), determines a parent event identifier for thefirst event, and identifies one of the set of the collections of recordsbased on the parent event identifier. The identify set of collections ofrecords module 416 may further function to provide the identify anomalymodule 414 with collections of records 424 for analysis to identify theperformance anomaly 422.

The determine significance module 418 determines whether a reliablesignificance indication 428 of the performance anomaly is determinablebased on at least some of the set of collections of records 426. Thedetermine significance module 418 functions to determine whether thereliable significance indication 428 of the performance anomaly 422 isdeterminable by at least one of a variety of approaches. In a firstapproach, the determine significance module 418 determines that thesignificance indication 428 is reliable when the performance anomaly 422corresponds to a de-minimis performance degradation of the DSN 22. Forexample, the determine significance module 418 quantifies a performancedegradation of the DSN 22 and determines that the performancedegradation is less than a performance degradation threshold associatedwith the de-minimis performance degradation.

In a second approach, the determine significance module 418 determinesthat the significance indication 428 is reliable when the performanceanomaly 422 corresponds to an undesired performance degradation of theDSN 22. For example, the determine significance module 418 quantifiesanother performance degradation of the DSN 22 and determines that theother performance degradation is greater than an undesired performancedegradation threshold. In a third approach, the determine significancemodule 418 determines that the significance indication 428 is unreliablewhen the performance anomaly 422 corresponds to a performancedegradation that is greater than the de-minimis performance degradationand is less than the undesired performance degradation.

When the reliable significance indication 428 of the performance anomaly422 is not determinable, the modify records module 420 modifies datacollection criteria for one or more of the sets collections of records.The modify records module 420 functions to modify data collectioncriteria for one or more of the sets collections of records by at leastone of a variety of approaches. In a first approach, the modify recordsmodule 420 adds a log record 430 to one or more records of the set ofcollections of records 426. A log record 430 includes one or more of areporting entity identifier, a state identifier associated with a stateof processing, a timestamp corresponding to the state, a statedescriptor corresponding to the state, and state parameterscorresponding to the state. The adding includes directly generating thelog record 430, sending a request to a reporting entity associated withthe performance anomaly 422 to generate the log record 430, and sendinga request to the reporting entity to generate another log record 430 foreach additional record or modification of an existing record associatedwith the performance anomaly 422.

In a second approach, the modify records module 420 adds a statisticsrecord 432 to the one or more records of the set of collections ofrecords. The statistics record 432 includes one or more of a reportingentity identifier, a step identifier associated with a step ofprocessing, a timestamp corresponding to the step, and one or moredescriptors corresponding to the step. A descriptor of the one or moredescriptors includes a type and a value. The adding includes directlygenerating the statistics record 432, sending a request to a reportingentity associated with the performance anomaly 422 to generate thestatistics record 432, and sending a request to the reporting entity togenerate another statistics record 432 for each additional record ormodification of an existing record associated with the performanceanomaly 422.

In a third approach, the modify records module 420 modifies persistence434 of the one or more records of the set of collections of records. Forexample, the modify records module 420 increases persistence when thereliable significance indication 428 of the performance anomaly 422 isnot determinable to extend a collection time period of more information.As another example, the modify records module 420 decreases persistencewhen the reliable significance indication 428 of the performance anomaly422 is determinable to shorten the collection time period of moreinformation. As yet another example, the modify records module 420decreases persistence by deleting one or more records associated withthe performance anomaly 422 when the reliable significance indication428 of the performance anomaly 422 is determinable.

FIG. 18B is a flowchart illustrating an example of modifying eventrecords in accordance with the present invention. The method begins atstep 440 where a processing module (e.g., a dispersed storage (DS)processing module of a DS managing unit) identifies a performanceanomaly within a dispersed storage network (DSN). The processing moduleidentifiers the performance anomaly by at least one of identifying adeviation of performance of one or more of the set of DS units from anexpected DS unit performance trend and identifying another deviation ofthe DS processing module from an expected DS processing moduleperformance trend.

The method continues at step 442 where the processing module identifiesa set of collections of records corresponding to the performanceanomaly. One of the set of collections of records includes an eventrecord including information regarding an event, a first recordincluding information regarding a DS processing module processing anevent request to produce a plurality of sub-event requests, and aplurality of records including information regarding a set of DS unitsprocessing the plurality of sub-event requests. The event includes auser access operation or a system administrative operation initiated bya device affiliated with the DSN. The event request includes a requestregarding the event. The identifying the set of collections of recordsincludes the processing module identifying events associated with theperformance anomaly. The identifying the set of collections of recordsfurther includes, for one of the events, three steps. In a first step,the processing module identifies the one of the events corresponding toa user access operation regarding the performance anomaly. In a secondstep, the processing module determines a parent event identifier for thefirst event. In a third step, the processing module identifies one ofthe set of the collections of records based on the parent eventidentifier.

The method continues at step 444 where the processing module determineswhether a reliable significance indication of the performance anomaly isdeterminable based on at least some of the set of collections ofrecords. The determining whether a reliable significance indication ofthe performance anomaly is determinable includes at least one of avariety of methods. In a first method, the processing module determinesthat the significance indication is reliable when the performanceanomaly corresponds to a de-minimis performance degradation of the DSN.In a second method, the processing module determines that thesignificance indication is reliable when the performance anomalycorresponds to an undesired performance degradation of the DSN. In athird method, the processing module determines that the significanceindication is unreliable when the performance anomaly corresponds to aperformance degradation that is greater than the de-minimis performancedegradation and is less than the undesired performance degradation.

When the reliable significance indication of the performance anomaly isnot determinable, the method continues at step 446 where the processingmodule modifies data collection criteria for one or more of the setscollections of records. The modifying data collection criteria for oneor more of the sets collections of records include at least one of avariety of approaches. In a first approach, the processing module adds alog record to one or more records of the set of collections of records.The log record includes one or more of a reporting entity identifier, astate identifier associated with a state of processing, a timestampcorresponding to the state, a state descriptor corresponding to thestate, and state parameters corresponding to the state. In a secondapproach, the processing module adds a statistics record to the one ormore records of the set of collections of records. The statistics recordincludes one or more of a reporting entity identifier, a step identifierassociated with a step of processing, a timestamp corresponding to thestep, and one or more descriptors corresponding to the step. In a thirdapproach, the processing module modifies persistence of the one or morerecords of the set of collections of records. For example, theprocessing module determines that a time duration to save records is 48hours based on an error priority table lookup when the performanceanomaly is associated with a low voltage detection associated with a DSunit. As another example, the processing module determines that the timeduration to save records is 30 days based on an error priority tablelookup when the performance anomaly is associated with a failing memoryof a DS unit.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures Such a memory deviceor memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc. that may usethe same or different reference numbers and, as such, the functions,steps, modules, etc. may be the same or similar functions, steps,modules, etc. or different ones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method comprising: receiving an error messageincluding first information associated with a first reported error and atime at which the first reported error was detected; identifying firststored event records associated with second reported errors, the firststored event records including second information describing previouslyreported errors that occurred within a first predetermined time prior tothe time at which the first reported error was detected; determining,based on the first information and the second information, whether acorrelation exists among one or more of the previously reported errorsand the first reported error; and in response to determining that thecorrelation exists, generating an error correlation report predictingoccurrence of a third error.
 2. The method of claim 1, wherein: thesecond information includes links to log records and statistics records.3. The method of claim 2, wherein determining whether a correlationexists among one or more of the previously reported errors and the firstreported error includes: identifying, based on the first stored eventrecords, the log records, and the statistics records, a particular eventthat historically precedes the first reported error.
 4. The method ofclaim 1, further comprising: identifying the first stored event recordsbased, at least in part, on a time stamp associated with the firststored event records.
 5. The method of claim 1, further comprising:identifying second stored event records associated with third reportederrors, the second stored event records including third informationdescribing subsequently reported errors that occurred within a secondpredetermined time after the time at which the first reported error wasdetected, and links to log records and statistics records.
 6. The methodof claim 5, further comprising: determining, based on the firstinformation and the third information, whether a correlation existsamong one or more of the subsequently reported errors and the firstreported error.
 7. The method of claim 5, further comprising:identifying the second stored event records based, at least in part, ona time stamp associated with the second stored event records.
 8. Anon-transitory computer readable medium tangibly embodying a program ofinstructions configured to be stored in a memory and executed by aprocessor, the program of instructions comprising: at least oneinstruction to receive an error message including first informationassociated with a first reported error and a time at which the firstreported error was detected; at least one instruction to identify firststored event records associated with second reported errors, the firststored event records including second information describing previouslyreported errors that occurred within a first predetermined time prior tothe time at which the first reported error was detected; at least oneinstruction to determine, based on the first information and the secondinformation, whether a correlation exists among one or more of thepreviously reported errors and the first reported error; and at leastone instruction to generate, in response to determining that thecorrelation exists, an error correlation report predicting occurrence ofa third error.
 9. The non-transitory computer readable medium of claim8, wherein: the second information includes links to log records andstatistics records.
 10. The non-transitory computer readable medium ofclaim 9, wherein the at least one instruction to determine whether acorrelation exists among one or more of the previously reported errorsand the first reported error includes: at least one instruction toidentify, based on the first stored event records, the log records, andthe statistics records, a particular event that historically precedesthe first reported error.
 11. The non-transitory computer readablemedium of claim 8, further comprising: at least one instruction toidentify the first stored event records based, at least in part, on atime stamp associated with the first stored event records.
 12. Thenon-transitory computer readable medium of claim 8, further comprising:at least one instruction to identify second stored event recordsassociated with third reported errors, the second stored event recordsincluding third information describing subsequently reported errors thatoccurred within a second predetermined time after the time at which thefirst reported error was detected, and links to log records andstatistics records.
 13. The non-transitory computer readable medium ofclaim 12, further comprising: at least one instruction to determine,based on the first information and the third information, whether acorrelation exists among one or more of the subsequently reported errorsand the first reported error.
 14. The non-transitory computer readablemedium of claim 12, further comprising: at least one instruction toidentify the second stored event records based, at least in part, on atime stamp associated with the second stored event records.
 15. A systemcomprising: a processor; memory coupled to the processor; and a programof instructions configured to be stored in a memory and executed by aprocessor, the program of instructions comprising at least oneinstruction to receive an error message including first informationassociated with a first reported error and a time at which the firstreported error was detected; at least one instruction to identify firststored event records associated with second reported errors, the firststored event records including second information describing previouslyreported errors that occurred within a first predetermined time prior tothe time at which the first reported error was detected; at least oneinstruction to determine, based on the first information and the secondinformation, whether a correlation exists among one or more of thepreviously reported errors and the first reported error; and at leastone instruction to generate, in response to determining that thecorrelation exists, an error correlation report predicting occurrence ofa third error.
 16. The system of claim 15, wherein: the secondinformation includes links to log records and statistics records. 17.The system of claim 16, wherein the at least one instruction todetermine whether a correlation exists among one or more of thepreviously reported errors and the first reported error includes: atleast one instruction to identify, based on the first stored eventrecords, the log records, and the statistics records, a particular eventthat historically precedes the first reported error.
 18. The system ofclaim 15, further comprising: at least one instruction to identify thefirst stored event records based, at least in part, on a time stampassociated with the first stored event records.
 19. The system of claim18, further comprising: at least one instruction to identify secondstored event records associated with third reported errors, the secondstored event records including third information describing subsequentlyreported errors that occurred within a second predetermined time afterthe time at which the first reported error was detected, and links tolog records and statistics records.
 20. The system of claim 18, furthercomprising: at least one instruction to determine, based on the firstinformation and the third information, whether a correlation existsamong one or more of the subsequently reported errors and the firstreported error.